Evripidis Bantis, Michael P. Clements, Andrew Urquhart
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引用次数: 2
Abstract
In this paper we consider the value of Google Trends search data for nowcasting (and forecasting) GDP growth for a developed economy (the U.S.) and an emerging-market economy (Brazil). Our focus is on the marginal contribution of big data in the form of Google Trends data over and above that of traditional predictors, and we use a dynamic factor model to handle the large number of potential predictors and the “ragged-edge” problem. We find that factor models based on economic indicators and Google “categories” data provide gains compared to models that exclude this information. The benefits of using Google Trends data appear to be broadly similar for Brazil and the U.S., and depend on the factor model variable-selection strategy. Using more disaggregated Google Trends data than its “categories” is not beneficial.
期刊介绍：
The International Journal of Forecasting is a leading journal in its field that publishes high quality refereed papers. It aims to bridge the gap between theory and practice, making forecasting useful and relevant for decision and policy makers. The journal places strong emphasis on empirical studies, evaluation activities, implementation research, and improving the practice of forecasting. It welcomes various points of view and encourages debate to find solutions to field-related problems. The journal is the official publication of the International Institute of Forecasters (IIF) and is indexed in Sociological Abstracts, Journal of Economic Literature, Statistical Theory and Method Abstracts, INSPEC, Current Contents, UMI Data Courier, RePEc, Academic Journal Guide, CIS, IAOR, and Social Sciences Citation Index.
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